dmnorm_sgv

<code>dmnorm_sgv</code> (and <code>rmnorm_sgv</code>) calculate the approximate SGV
likelihood for a fixed set of parameters (i.e., the U matrix). Finally,
the distributions must be registered within <code>nimble</code>.

Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <doi:10.48550/arXiv.1702.00434>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the "nimble" package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.

Daniel Turek

BayesNSGP

Bayesian Analysis of Non-Stationary Gaussian Process Models

Mark Risser

dmnorm_sgv function

<dl><dt>x</dt>
<dd>Vector of measurements</dd>
<dt>mean</dt>
<dd>Vector of mean valiues</dd>
<dt>U</dt>
<dd>Matrix of size N x 3; representation of a sparse N x N Cholesky
of the precision matrix. The first two columns contain row and column 
indices, respectively, and the last column is the nonzero elements of the
matrix.</dd>
<dt>N</dt>
<dd>Number of measurements in x</dd>
<dt>k</dt>
<dd>Number of neighbors for the SGV approximation.</dd>
<dt>log</dt>
<dd>Logical; should the density be evaluated on the log scale.</dd></dl>

Arguments

Function for the evaluating the SGV approximate density. — dmnorm_sgv

<dl>

<dt>x</dt>
<dd>Vector of measurements</dd>


<dt>mean</dt>
<dd>Vector of mean valiues</dd>


<dt>U</dt>
<dd>Matrix of size N x 3; representation of a sparse N x N Cholesky
of the precision matrix. The first two columns contain row and column 
indices, respectively, and the last column is the nonzero elements of the
matrix.</dd>


<dt>N</dt>
<dd>Number of measurements in x</dd>


<dt>k</dt>
<dd>Number of neighbors for the SGV approximation.</dd>


<dt>log</dt>
<dd>Logical; should the density be evaluated on the log scale.</dd>

</dl>

dmnorm_sgv: Function for the evaluating the SGV approximate density.

Description

Usage

Value

Arguments